Tag Archives: demography

Certain death? Black-White death dispersions

New research report, after rumination.

Knowing the exact moment of death is a common fantasy. How would it change your life? Here’s a concrete example: when I got a usually-incurable form of cancer, and the oncologist told me the median survival for my condition was 10 to 20 years, I treated myself to the notion that at least I wasn’t going to the dentist anymore (6 years later, with no detectable cancer, I’m almost ready to give up another precious hour to dentistry).

I assume most people don’t want to die at a young age, but is that because it makes life shorter or because it makes them think about death sooner? When a child discovers a fear of death, isn’t it tempting to say, “don’t worry: you’re not going to die for a long, long time”? The reasonable certainty of long life changes a lot about how we think and interact (one of the many reasons you can’t understand modernity without knowing some basic demography). I wrote in that cancer post, “Nothing aggravates the modern identity like incalculable risk.” I don’t know that’s literally true, but I’m sure there’s some connection between incalculability and aggravation.

Consider people who have to decide whether to get tested for the genetic mutation that causes Huntington’s disease. It’s incurable and strikes in what should be “mid”-life. Among people with a family history of Huntington’s disease, Amy Harmon reported in the New York Times, the younger generation increasingly wants to know:

More informed about the genetics of the disease than any previous generation, they are convinced that they would rather know how many healthy years they have left than wake up one day to find the illness upon them.

The subject of Harmon’s story set to calculating (among other things) whether she’d finish paying off her student loans before her first symptoms appeared.

The personal is demographic

So what is the difference between two populations, one of which has a greater variance in age at death than the other? (In practice, greater variance usually means more early deaths, and the risk of a super long life probably isn’t as disturbing as fear of early death.) Researchers call the prevalence of early death — as distinct from a lower average age at death — “life disparity,” and it probably has a corrosive effect on social life:

Reducing early-life disparities helps people plan their less-uncertain lifetimes. A higher likelihood of surviving to old age makes savings more worthwhile, raises the value of individual and public investments in education and training, and increases the prevalence of long-term relationships. Hence, healthy longevity is a prime driver of a country’s wealth and well-being. While some degree of income inequality might create incentives to work harder, premature deaths bring little benefit and impose major costs. (source)

That’s why reducing life disparity may be as important socially as increasing life expectancy (the two are highly, but not perfectly, correlated).

New research

Consider a new paper in Demography by Glenn Firebaugh and colleagues, “Why Lifespans Are More Variable Among Blacks Than Among Whites in the United States.”

I previously reported on the greater life disparity and lower life expectancy among Blacks than among Whites. Here is Firebaugh et al’s representation of the pattern (the distribution of 100,000 deaths for each group):

bwdeaths

Black deaths are earlier, on average, but also more dispersed. The innovation of the paper is that they decompose the difference in dispersion according to the causes of death and the timing of death for each cause. The difference in death timing results from some combination of three patterns. Here’s their figure explaining that (to which I added colors and descriptions, as practice for teaching myself to use an illustration program — click to enlarge):

bw death disparities

The overall difference in death timing can result from the same causes of death, with different variance in timing for each around the same mean (spread); different causes of death, but with the same age pattern of death for each cause (allocation); and the same causes of death, but different average age at death for each (timing). Above I said greater variability in life expectancy usually means more early deaths, but with specific causes that’s not necessarily the case. For example, one group might have most of its accidental deaths at young ages, while another has them more spread over the life course.

Overall, the spread effect matters most. They conclude that even if Blacks and Whites died from the same causes, 87% of the difference in death timing would persist because of the greater variance in age at death for every major cause. There are differences in causes, but those mostly offset. Especially dramatic are greater variance in the timing of heart disease (especially for women), cancer, and asthma (presumably more early deaths), The offsetting causes are higher Black rates of homicide (for men) and HIV/AIDS deaths, versus high rates of suicide and accidental deaths among White men (especially drug overdoses).

The higher variance in causes of death seems consistent with problems of disease prevention and disparities in treatment access and quality. (I’m not expert on this stuff, so please don’t take it exclusively from me — read the paywalled paper or check with the authors if you want to pursue this.)

Are these differences in death timing enough to create differences in social life and outlook, or health-related behavior, between these two groups? I don’t know, but it’s worth considering.

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So you want to know the Asian divorce rate (save the ACS marital events edition)

One of the most popular posts ever on this blog is about Asian incomes, and especially the variation in average incomes across Asian national-origin groups and cities. Turns out the diverse Asian groups have different divorce rates as well. Why not? It would be nuts to assume the immigrants and their descendants from everywhere from Bangladesh to Japan had common family practices and behavior.

We can figure this out with the American Community Survey (see below; data from which is provided by IPUMS.org). The ACS is big enough to measure divorce rates for Asian subgroups if you pool together a few years — for this I use the 2008-2012 file. For reliability, here I am just showing those groups that had a sample of at least 1,000 married people. And I’m including as separate groups those that selected more than one “race” – Japanese-White, Korean-White, and Filipino-White (you’ll see why I separated them out). Note these are multiple-race individuals, not couples in which the two spouses reported different races.

The national refined divorce rate — divorces per 1,000 people — fell from 20 to 18 at the start of the recession in 2008, before rebounding back up to 19 by 2012. So compare these numbers with about 19 as the national average divorce rate (click to enlarge).

asian divorce rates 08-12.xlsx

Look at that spread! Now won’t you feel a little foolish for even asking what the “Asian” divorce rate is? I leave the interpretation to the relevant experts (media note: but I’ll be happy to speculate if it will help you get your story past the editor).

A further wrinkle: gender. Unfortunately, because the ACS is a household survey, if someone is divorced, the person they divorced is usually not living in the same household, which means we don’t know who they divorced (or even the other spouse’s gender!). Naturally, men and women in the same ethnic group can have different divorce rates to the extent that they marry outside their own group (or get gay divorced at different rates).

So here are the divorce rates for the same groups, but separately by gender. Groups above the line have higher divorce rates for men (Pakistanis, Cambodians), those below the line have higher divorce rates for women (Korean, Vietnamese, Korean-White). Click to enlarge:
asiandivorcegenderBy now you’ve realized what a wonderful treasure-trove of data this is for understanding the incredibly expanding family complexity that pulses all around us. Or, as they say, “Pretty nice data you got there. I’d hate to see something happen to it.” Read on.

Speak up

Last week I reported “millennial” generation divorce rates for 25 metropolitan areas. That’s something you can only get from the very large American Community Survey (because we have no national registry of marital events).

In addition to local areas, however, the vast size of the ACS lets us drill down into very small groups in the population — like small Asian subgroups. For another example, remember the big ruckus over same-sex marriage (you know, homogamy)? I for one would love to have good national data on same-sex marriage patterns when the equality-deniers finally lope back into their caves and the dust settles.

But now the feds are proposing to scrap the marital events (did you get married, divorced, or widowed last year?) and marital history (how many times have you been married, and when was the last time?) questions from the ACS just to save a few million dollars. I hope you’ll help demographic science convince them not to. (In the previous post I listed a bunch of divorce facts we only know because of the ACS questions.)

The information about the planned cuts to the American Community Survey is here: https://www.federalregister.gov/articles/2014/10/31/2014-25912/proposed-information-collection-comment-request-the-american-community-survey-content-review-results:

Direct all written comments to Jennifer Jessup, Departmental Paperwork Clearance Officer, Department of Commerce, Room 6616, 14th and Constitution Avenue NW., Washington, DC 20230 (or via the Internet at jjessup@doc.gov).

Comments will be accepted until December 30.

* Contrary to popular belief, there is no “Asian” category on the Census/ACS form. People are identified as Asian if they pick any of the Asian national origins listed on the “race” question. It’s all pretty American-exceptional. Here is the question, from this form:

acs2010raceq

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Top 25 cities for Millennial divorce (save the American Community Survey marital history and events questions edition)

First some news, then the urgent story behind the news.

All marriages and divorces are local. Whether and when people marry is dependent on who they meet and the conditions under which their relationships develop — social, economic, and even political. And divorce depends on local factors as well, such as the likelihood or feasibility of meeting alternative partners, the costs and consequences of divorce, and the norms and laws regulating divorce. (The same is true for forming and ending non-marital relationships, but those are harder to measure and their dynamics are different).

The Millennial Divorce Capital

So, I know the question you’re dying to have answered is: Where do Millennials get divorced? This question is so compelling that I’ve suspended my normal objection to arbitrary generational definitions, and let Mellennials be defined as anyone born in 1980 or later — so this is the divorce rate among people roughly ages 15 to 31 in the 2009-2011 American Community Survey.

Measuring divorce is complicated, because you’ve got a lot of choices. Here are two simple ways: The number of divorces among Millennials occurring in the previous year per 1,000 Millennials in the population (the crude Millennial divorce rate), and the number of those divorces per 100 married Millennials (the refined Millennial divorce rate). Think of the crude rate as the chance of meeting a Millennial who just got divorced walking down the street, and the refined rate as the chance that one of your married friends just got divorced. I’ve ranked them by the refined rate for this table, which we can use to crown Portland, Oregon the Millennial Divorce Capital of the United States (it’s #1 on both measures).

div acs metro demo.xlsx

This is just the 25 largest Millennial population centers, for which we have the most reliable estimates of divorce rates. Nationally, 6.2 out of every 1,000 living Millennials reported getting divorced in the previous 12 months.

It’s complicated

The differences in the two rates I show can be very important. For example, if I expanded the list to the top 50, you would see that the city in which you are most likely to bump into a divorced Millennial at random (spilling your non-caffeinated beverage) is Salt Lake City, where an astonishing 9.7 out of every 1,000 Millennials got divorced in the past year. That’s not because they love divorce, however, it’s because they love marriage. An amazing 34% of Salt Lake City Millennials in 2009-2011 had already been married, compared to just 23% in Divorce Capital Portland.

On the other hand, it’s not just that more married people means more people available for divorce. It’s also the case that early marriage increases the risk of divorce. And more than that, places where early marriage is common have higher rates, even for people who get married at older ages. In this figure I show an adjusted divorce rate (technically, the predicted chance of divorcing in year 5 given marrying at age 23) by the average age at first marriage in each metro area: Divorce is less common in the late-marriage cities*:

div acs metro demo ageat

(Note that, to produce this figure, you need a survey that asks millions of people how many times they’ve been married, the year they most recently got married, and whether they got divorced in the past year. You don’t just type this into a Google search box.)

But wait, I’m afraid it’s more complicated than that. And here I’m moving toward the urgent story behind the news.

People move around. Divorce may occur in a split second, but what demographers call “relationship dissolution” unfolds over time. People move after they divorce, they divorce after they move, and they may even get divorced in places other than where they live. The ACS data I’m using here help sort this out. This divorce incidence measure is based on a survey question, not a legal record. As with all the other questions on the survey (age, race, income, education, etc.), we more or less have to trust the answers people give (some implausible answers are edited out by the Census Bureau). If we really want to understand how and where divorce (or marriage) happens, we need to be creative and careful, and use the best data. And this is the best data.

Here’s a simple illustration. This figure shows the percentage of two groups of Millennials in each state who arrived in the past year: Those who are married, and those who just got divorced. For example, in Oregon, home of the Millennial Divorce Capital, 17% of the divorced Millennials lived in a different state last year. So either moving to Oregon led to their divorce, or their divorce led to an interstate migration. In contrast, only 7% of Oregon’s married Millennial population just got there (click to enlarge):

aca-state-divorce-movers

The red line is the diagonal, so states above the line — most states — have more divorced arrivals than married arrivals (I excluded a few states with few cases in the data). There are, naturally, a lot of fascinating ways you might approach these questions. Which brings me to the urgent news.

Save the American Community Survey marital events and history data

I know from experience that some of you are thinking things like, “Break it down by race!”, “What about gay couples?”, and “What about hypergamy?” If you want those answers, get out your wallets. This information doesn’t just happen, it’s garnered through a massive federal data collection, without which our ability to know ourselves and our society would be severely compromised. And that’s what might be about to happen.

The data I just showed came from the American Community Survey (ACS), the large Census Bureau survey that replaced the “long form” of the decennial census in the 2000s. (The data are wonderfully prepared and distributed by the good people at IPUMS.org.) Unlike a simple national random survey — which is a major undertaking in itself — the ACS uses a sophisticated rotating geographic design that samples from all around the country to gather the information we need for all levels of geographic detail – down to the neighborhood.

Filling out this survey, in 3.5 million households, is estimated to take about 2.3 million hours of the American people’s time and cost a fortune. Now the federal government is reviewing the different parts of the survey looking for unnecessary parts, and they have identified 7 questions that could be cut, including the ones I’ve been using here: marital events (did you get married, divorce, or widowed in the last year), marital history (how many times have you been married, when did you get married most recently), and a couple others. So I’m trying to convince you to submit a public comment urging them not to make the cuts.

Why do we need this?

Believe it or not, there is no national count of marriages and divorces. That’s right, your government cannot tell you how many legal marriages and divorces there are. They used to collect this from every state, but now they don’t. States collect this information, but it’s not standardized, and it’s not collected together. And, even if it were, we wouldn’t be able to analyze it with all the detail I’ve used here — using marriage duration, age at marriage, and other important factors. So, even at the national level, this is all we have.

However, just for national marriage and divorce statistics, we wouldn’t need the ACS. We could use a smaller survey, like the Current Population Survey or others. If they wanted to work out one of those alternatives before canceling these questions, that would be OK for national statistics.

However, for smaller populations — state and local populations, minority groups, gay and lesbian couples — there is no alternative. If we lose these questions on the ACS, we lose the ability to do all that. Unfortunately, there is no legal or legislative mandate to collect this information down to the local level, which is why it’s on the chopping block. It’s just super interesting and important, not legally required. So we need to communicate that up the chain of command and hope they listen.

To help motivate and inform you toward that end, here’s a list of what we would not know about divorce without the ACS marital events and history questions, and then the information for contacting the federal government with your comment. These are just from my blog, I haven’t done an exhaustive search. The point is not that I’ve done so much, but that there is so much of vital importance that we can learn from this data.

  • The refined divorce rate in 2012 was 19 per 1,000 married people.
  • The overall projected divorce rate for couples marrying in 2012 is about 50%. This requires using marriage, divorce, and widowhood incidence to calculate competing risks. You need all the ACS questions for that.
  • We lost about 150,000 divorces during and after the recession. Then divorce rebounded to catch up to its (declining) trend. That’s the result of my analysis published in Population Research and Policy Review, which relied on a model using all the ACS individual data in all 50 states.
  • People with disabilities are much more likely to get divorced than people without. The magnitude of the difference depends on the type of disability.
  • Divorce rates in first marriages are more than three-times as high for Black women as for Asian women in the U.S.
  • The 2008-09 refined divorce rate by state was correlated with Google searches for “colt 45 automatic” at .86 (on a scale of -1 to 1).
  • The 2011 crude divorce rate by state was correlated with Google searches for “vasectomy reversal” at .79.
  • The changing pattern of same-sex marriage across states and local areas. We don’t know this yet, but we should, and we’ll want to, and the ACS is the only way we’ll be able to.

Speak up

The information about the planned cuts to the American Community Survey is here: https://www.federalregister.gov/articles/2014/10/31/2014-25912/proposed-information-collection-comment-request-the-american-community-survey-content-review-results:

Direct all written comments to Jennifer Jessup, Departmental Paperwork Clearance Officer, Department of Commerce, Room 6616, 14th and Constitution Avenue NW., Washington, DC 20230 (or via the Internet at jjessup@doc.gov).

Comments will be accepted until December 30.

* Here’s the mixed-effect multilevel regression testing the relationship between average age at marriage (meanagemarr) and odds of divorcing, controlling for age at marriage and duration of marriage, for 262,269 married people in 283 metro areas:

acs-metro-div-reg

If you want to see serious research into the effects of age at marriage, local age at marriage, and religion, on divorce, this paper by Glass and Levchak is the right place to start.

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Data snapshot: Married before

Among newlyweds in the United States, 30% have been married before. Here’s the breakdown by state (click to enlarge):

married-before-2012-marriages

 

Here’s a list of states and DC, from highest to lowest percent married before:

married-before-2012-marriages-table

And here is the Google search most highly correlated with this pattern: Kerrelyn Sparks (correlation = .83):

kerrelynsparks

The top 100 correlated searches is shot through with romance and fantasy novels: Lynsay Sands, romance series, Sherrilyn Kenyon, vampire book, fever series, Jeaniene Frost.

Coming soon: Crouching Tiger, Forbidden Vampire (and your next marriage?):

crouching

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Life expectancy update, disparity edition

The good news is that U.S. life expectancy is at a record high, 78.8 as of 2012.

What about life disparity — the inequality in life expectancy? With the economic crisis and rise in income inequality, it would be great to know. However, the National Center for Health Statistics hasn’t released detailed life tables with data more recent than 2008, so I can’t yet update the data for the analysis I did last year, so here it is reposted instead:

Life Expectancy, Life Disparity

Reposted from July 23, 2013

In 2008 the life expectancy at birth in the U.S. was 78.1. That means that if a group children born in 2008 lived every year of their lives exposed to the risks of death observed in 2008, their average lifespan would be 78.1 years. But those who made it to age 60 would live an average of 22.7 more years, for a total of 82.7. And those who live to age 99 would live an average of 2.4 more years, for an average of 101.4.

So “life expectancy” as commonly used is not a prediction of how long today’s babies will live — since we hope the future is better than living 2008 over and over — and it’s not a prediction of how long your elderly loved ones will live.

Life disparity

Life expectancy — for any age — is a measure of central tendency: the average number of years of life remaining. And so there is a dispersion around that mean. That dispersion is inequality. A very nice article in the open-access journal BMJ Open, by James Vaupel, Zhen Zhang and Alyson A van Raalte, describes the measure of life disparity. It’s complicated, but a neat tool.

Life disparity is the average number of years people are expected to live when they die. For example, in the U.S. in 2008 an infant who died on the first day of life died 78.1 years early. And a 78-year-old who died, counterintuitively, died 10 years early (since the life expectancy at 78 is 10). To understand what this measure means, consider that if everyone died at exactly 78.1 years of age, life expectancy would be unchanged but life disparity would be 0. On the other hand, the greatest life disparity would occur if all early occurred at age 0.

Life disparity and life expectancy usually go together. That’s because reducing early deaths has the biggest effect on both measures. Here is the cool figure from that paper:

The association between life disparity in a specific year and life expectancy in that year for males in 40 countries and regions, 1840–2009. The black triangle represents the USA in 2007; the USA had a male life expectancy 3.78 years lower than the international record in 2007 and a life disparity 2.8 years greater. The brown points denote years after 1950, the orange points 1900–1949 and the yellow points 1840–1900. The light blue triangles represent countries with the lowest life disparity but with a life expectancy below the international record in the specific year; the dark blue triangles indicate the life expectancy leaders in a given year, with life disparities greater than the most egalitarian country in that year. The black point at (0,0) marks countries with the lowest life disparity and the highest life expectancy. During the 170 years from 1840 to 2009, 89 holders of record life expectancy also enjoyed the lowest life disparity.

The association between life disparity in a specific year and life expectancy in that year for males in 40 countries and regions, 1840–2009. The black triangle represents the USA in 2007; the USA had a male life expectancy 3.78 years lower than the international record in 2007 and a life disparity 2.8 years greater. The brown points denote years after 1950, the orange points 1900–1949 and the yellow points 1840–1900. The light blue triangles represent countries with the lowest life disparity but with a life expectancy below the international record in the specific year; the dark blue triangles indicate the life expectancy leaders in a given year, with life disparities greater than the most egalitarian country in that year. The black point at (0,0) marks countries with the lowest life disparity and the highest life expectancy. During the 170 years from 1840 to 2009, 89 holders of record life expectancy also enjoyed the lowest life disparity.

Countries at the bottom left (0,0) have both the world’s highest life expectancy and the lowest life disparity in the world for that year, which occurred 89 times over 170 years. Countries below the diagonal have relatively low life disparity given their life expectancy; those above the diagonal (like the U.S.) have higher-than-expected life disparity for their level of life expectancy. In our case that reflects the fact that we do a pretty good job keeping old people alive, but let too many young people die.

U.S. improvement

The good news is that life expectancy is increasing in the U.S. (and most other places), and that the inequality between Blacks and Whites is getting smaller, as reported by the National Center for Health Statistics. That is, the Black-White inequality in average expectation of life at birth has shrunk.

The mixed news is that life disparity is much higher for Blacks than Whites — but that gap is falling as well. Here are those numbers for 1998 and 2008 (I did the life disparity calculations from this and this, and will happily share the spreadsheet). Click to enlarge:

expectancydisparity

So Black deaths are more dispersed than White deaths: 14 and 13 for males and females, compared with 12 and 11. For comparison, the Swedish female life disparity is 9. What does a higher disparity mean? Generally, a larger share of early deaths. That’s why the race gap in life expectancy at birth is greater than the race gap in life expectancy at older ages — average 65-year-old Whites and Blacks have more similar life expectancies than do infants.

Why is life disparity more interesting than life expectancy alone, and how does this help explain Black-White inequality in the U.S.? For one thing, high life disparity indicates either relatively unhealthy or dangerous living conditions at younger ages. So it’s partly a measure of the quality of life. Vaupel et al. add:

Reducing early-life disparities helps people plan their less-uncertain lifetimes. A higher likelihood of surviving to old age makes savings more worthwhile, raises the value of individual and public investments in education and training, and increases the prevalence of long-term relationships. Hence, healthy longevity is a prime driver of a country’s wealth and well-being. While some degree of income inequality might create incentives to work harder, premature deaths bring little benefit and impose major costs. Moreover, equity in the capability to maintain good health is central to any larger concept of societal justice.

I think what they say about differences between countries would apply to differences between groups within a society as well.

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This word ‘generation,’ I do not think it means what you think it means

The people who make up these things drive me bananas.

NPR launched a new series on “millennials” yesterday, called “New Boom,” with this dramatic declaration: “There are more millennials in America right now than baby boomers — more than 80 million of us.”

The definition NPR gives for this generation is “people born between 1980 and 2000.” And it’s true there are more than 80 million of them. In fact, there are 91 million of them, according to the 2012 American Community Survey data you can get from IPUMS.org.* That’s OK, though, because there are only 76 million Baby Boomers, so the claim checks out.

But what’s a generation?

The Baby Boom was a demographic event. In 1946, after the end of World War II, the crude birth rate — the number of births per 1,000 population — jumped from 20.4 to 24.1, the biggest one-year change recorded in U.S. history. The birth rate didn’t fall back to its previous level until 1965. That’s why the Baby Boom went down in history as 1946 to 1964. Because that’s when it happened.

This figure shows the number of living people by birth year, and the crude birth rate recorded in each year, using the NPR definition of millennials (in red), compared with the baby boom (purple):

mellenials.xlsx

Even with population growth I reckon the people born in the years 1946-1964 might outnumber the self-promoting millennials if not for the weight of mortality pulling down the purple bars. But if the young NPR reporters want to brag about outnumbering a generation that is starting to lose its older members to old age (and who are, after all, their parents), then I guess the shoe fits.

The Baby Boom was not a generation. It was a cohort, “a group of people sharing a common demographic experience” (in this case birth during the same period). That demographic event happens to have lasted 18 years, which is unfortunate because that may have contributed to the tendency to declare “generations” of similar lengths.

The Pew Research people, who do lots of interesting work on social change that uses generational concepts, use these slightly different definitions for four generations: Silent Generation, born 1928-1945; the Baby Boom Generation, born 1946-1964; Generation X, born 1965-1980; Millennial Generation, born 1981 and later (Pew says “no chronological endpoint has been set for this group,” which is awkward because if they’re really still going, the oldest are 33 and they have children that are the same generation as themselves**). Ironic, isn’t it, that Pew constructs “Generation X” as the shortest of the four (some generation, a mere 16 years!) before declaring them “America’s neglected ‘middle child.’

Real generations rarely have starting and ending points on a population level. Populations usually just keeping having births every year in smooth patterns of increase or decrease without discrete edges, so generations overlap. Even in families it gets hard to nail down generations once you start moving horizontally; siblings born many years apart are in the same generation, but the cousins get all confused.

Meaningful cohorts, on the other hand, can be defined all over the place, such as: the people who graduated college during the Great Recession, people who introduced the Internet to their parents, and so on. These are not generations.

In 2010, when crisis was really in the air, I was on the NPR show The State of Things in North Carolina, discussing the Baby Boom (no audio online). After attempting to clarify the difference between a generation and a cohort, I offered this dramatic example of a cohort — people born in 1960 specifically:

So if you were born in 1960, graduated college in 1982, and entered the labor force in the middle of an awful recession, then managed to pull some kind of career together, got married and divorced, by the 90s it was time to be downsized already for the first time, you’re 40 in 2000, and it’s time for the dot-com bubble, you’re out of your job again, and here you are ready for your retirement, finally, you’ve been left in your own 401(k), having to put together your own pension, and of course now that’s in the tank and your house isn’t worth anything. So that insecurity and instability is really imprinted this group. We talk about the 60s, and civil rights and antiwar, and great music and everything, but that’s seeming like a long time ago now for people who are looking at retirement.

I don’t know if anyone actually had that experience, but it seems likely.

Anyway, if people really want to keep using these generation labels, and it seems unlikely to stop now given the marketing payoff from naming rights, than that’s the way it goes. But please don’t ask demographers to define them.

Notes

* This is a little different from the population estimates the Census Bureau produces, which are coded by age rather than year of birth. I use the ACS data because they report year of birth, and because it’s easier. The differences are very small.

** Thanks to Mo Willow for pointing this out.

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Diversity is the new normal

I have new briefing paper out today with the Council on Contemporary Families, titled “Family Diversity is the New Normal for America’s Children.” I’ll post news links soon. In the meantime:

I’m happy to provide high quality graphics.

Let me know what you think!

Reports and commentary:

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